Protecting and Preserving Protest Dynamics for Responsible Analysis

2026-04-06

AI Summary

This work addresses the high privacy risks associated with protest-related social media imagery, which can lead to individual identification and subsequent repression. To mitigate these concerns, the authors propose a responsible computational framework that, for the first time, jointly integrates privacy risk assessment, utility for downstream tasks, and group fairness. Leveraging conditional image synthesis, the framework generates diverse, realistic, and labeled synthetic protest images. The approach significantly reduces the risk of identity disclosure while preserving the analytical utility required for collective action research. Furthermore, fairness audits confirm equitable representation across demographic subgroups in the synthesized data, establishing a generalizable paradigm for privacy-preserving handling of sensitive visual content.

Abstract

Protest-related social media data are valuable for understanding collective action but inherently high-risk due to concerns surrounding surveillance, repression, and individual privacy. Contemporary AI systems can identify individuals, infer sensitive attributes, and cross-reference visual information across platforms, enabling surveillance that poses risks to protesters and bystanders. In such contexts, large foundation models trained on protest imagery risk memorizing and disclosing sensitive information, leading to cross-platform identity leakage and retroactive participant identification. Existing approaches to automated protest analysis do not provide a holistic pipeline that integrates privacy risk assessment, downstream analysis, and fairness considerations. To address this gap, we propose a responsible computing framework for analyzing collective protest dynamics while reducing risks to individual privacy. Our framework replaces sensitive protest imagery with well-labeled synthetic reproductions using conditional image synthesis, enabling analysis of collective patterns without direct exposure of identifiable individuals. We demonstrate that our approach produces realistic and diverse synthetic imagery while balancing downstream analytical utility with reductions in privacy risk. We further assess demographic fairness in the generated data, examining whether synthetic representations disproportionately affect specific subgroups. Rather than offering absolute privacy guarantees, our method adopts a pragmatic, harm-mitigating approach that enables socially sensitive analysis while acknowledging residual risks.

Problem

• privacy risk
• protest analysis
• identity leakage
• demographic fairness
• social media data

Innovation

• conditional image synthesis
• privacy-preserving analysis
• synthetic data generation
• protest dynamics
• demographic fairness